Update app.py

app.py

@@ -1,64 +1,59 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
+import torch
+import torch.nn as nn
+import sentencepiece as spm
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load tokenizers
+sp_pseudo = spm.SentencePieceProcessor(model_file="pseudo.model")
+sp_code = spm.SentencePieceProcessor(model_file="code.model")
+
+# Load the full saved model (architecture + weights)
+model_path = "pseudo-to-cpp-model.pth"  # Adjust path as needed
+model = torch.load(model_path, map_location=device)
+model.eval()
+model = model.to(device)
+
+
+def generate_code(pseudocode, max_len):
+    """Generate C++ code from pseudocode with streaming output."""
+    model.eval()
+    src = torch.tensor([sp_pseudo.encode_as_ids(pseudocode)], dtype=torch.long, device=device)
+    tgt = torch.tensor([[2]], dtype=torch.long, device=device)  # <bos_id>=2
+    
+    generated_tokens = [2]
     response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
+    with torch.no_grad():
+        for _ in range(max_len):
+            output = model(src, tgt)
+            next_token = output[:, -1, :].argmax(-1).item()
+            generated_tokens.append(next_token)
+            tgt = torch.cat([tgt, torch.tensor([[next_token]], device=device)], dim=1)
+            response = sp_code.decode_ids(generated_tokens)
+            yield response  # Yield partial output
+            if next_token == 5:  # <END> = 5
+                break
+    yield response  # Final output
+
+def respond(message, history, max_tokens):
+    """Wrapper for Gradio interface."""
+    # Ignore history since it's one-shot generation
+    for response in generate_code(message, max_tokens):
         yield response
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Gradio interface
 demo = gr.ChatInterface(
     respond,
+    chatbot=gr.Chatbot(label="Pseudocode to C++ Generator"),
+    textbox=gr.Textbox(placeholder="Enter pseudocode (e.g., 'for i from 1 to n, print i')", label="Pseudocode"),
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+        gr.Slider(minimum=10, maximum=1000, value=50, step=1, label="Max tokens"),
     ],
+    title="Pseudocode to C++ Transformer",
+    description="Convert pseudocode to C++ code using a custom transformer trained on the SPoC dataset.",
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()